A Novel Energy-Efficient Approach for Human Activity Recognition

Li, Zheng; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lü, Hai; Shi, Haibin; Zheng, Huiru

doi:10.3390/s17092064

Cited by 31 publications

(20 citation statements)

References 38 publications

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“…In early 2008, a computationally inexpensive methodology [18] for incorporating smoothing classification temporally was proposed, which can couple with any classifier with minimal training for classifying continuous sequences. The Hierarchical Support Vector Machine and Context-based Classification (HSVMCC) was proposed in [19] to recognize human activities when the sampling rate was less than the frequency of activities. These two methods utilize naive modification strategy, and do not consider the activity transition.…”

Section: B Related Workmentioning

confidence: 99%

W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

Wang

Liang

et al. 2020

IEEE Access

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The sensor-based human activity recognition has been wildly applied in behavior tracking, health monitoring, indoor localization etc. Using activity continuity to assist activity recognition is an important research issue, in which the activity transition matrix which describes the activity transformation in real scenarios is the most important parameter. Aiming at the problem that the current classic transition matrix learning algorithm cannot fuse weights of sample classification results, a weighted transition matrix learning algorithm is proposed in this paper. First, the basic definitions of an improved Hidden Markov Model (HMM) which fuses weights of classification results are given. Then, the recursive formula of transition matrix learning is derived, and the learning algorithm W-Trans is put forward. Finally, the proposed algorithm is simulated with the public data sets. The evaluation results show that the proposed algorithm outperforms the classical Baum-Welch algorithm under evaluation metrics of both the cosine similarity and the euler distance. By applying W-Trans to current activity recognition post-process methods, the advantage of our method is verified.

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Section: B Related Workmentioning

confidence: 99%

W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

Wang

Liang

et al. 2020

IEEE Access

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“…In this work, the authors use reinforcement learning to train a network on both video and motion information captured by sensors while penalizing actions that have high energy costs. Another approach to minimizing energy consumption in mobile devices when using an accelerometer for activity recognition is to minimize the sampling rate (Zheng et al, 2017). In Yan et al (2012) and Lee and Kim (2016), the authors investigate a network with adaptive features, sampling frequency, and window size for minimizing energy consumption during activity recognition.…”

Section: Related Workmentioning

confidence: 99%

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

Soures

Kudithipudi

2019

Front. Neurosci.

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Real-world applications such as first-person video activity recognition require intelligent edge devices. However, size, weight, and power constraints of the embedded platforms cannot support resource intensive state-of-the-art algorithms. Machine learning lite algorithms, such as reservoir computing, with shallow 3-layer networks are computationally frugal as only the output layer is trained. By reducing network depth and plasticity, reservoir computing minimizes computational power and complexity, making the algorithms optimal for edge devices. However, as a trade-off for their frugal nature, reservoir computing sacrifices computational power compared to state-of-the-art methods. A good compromise between reservoir computing and fully supervised networks are the proposed deep-LSM networks. The deep-LSM is a deep spiking neural network which captures dynamic information over multiple time-scales with a combination of randomly connected layers and unsupervised layers. The deep-LSM processes the captured dynamic information through an attention modulated readout layer to perform classification. We demonstrate that the deep-LSM achieves an average of 84.78% accuracy on the DogCentric video activity recognition task, beating state-of-the-art. The deep-LSM also shows up to 91.13% memory savings and up to 91.55% reduction in synaptic operations when compared to similar recurrent neural network models. Based on these results we claim that the deep-LSM is capable of overcoming limitations of traditional reservoir computing, while maintaining the low computational cost associated with reservoir computing.

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“…Several energy-efficient approaches for human activity recognition have been proposed. Energy-efficient approaches based on handcrafted features usually adjust the sampling rate [12,13] or use lightweight features to reduce energy consumption [19,20]. However, deep neural networks typically cannot flexibly process changes in the size of input data.…”

Section: Related Workmentioning

confidence: 99%

“…Many methods have been proposed to reduce the energy consumption of the activity classification process as part of the overall system. HAR methods based on handcrafted features mainly reduce the energy consumption by lowering or varying the sampling rate of the inertial sensors [12,13]. In addition, several methods using shallow networks to reduce the energy consumption of activity recognition engines based on DNNs were proposed [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

Jeong

Kim

2019

Sensors

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Human activity recognition (HAR), which is important in context awareness services, needs to occur continuously in daily life, owing to which an energy-efficient method is needed. However, because human activities have a longer cycle than HAR methods, which have analysis cycles of a few seconds, continuous classification of human activities using these methods is computationally and energy inefficient. Therefore, we propose segment-level change detection to identify activity change with very low computational complexity. Additionally, a fully convolutional network (FCN) with a high recognition rate is used to classify the activity only when activity change occurs. We compared the accuracy and energy consumption of the proposed method with that of a method based on a convolutional neural network (CNN) by using a public dataset on different embedded platforms. The experimental results showed that, although the recognition rate of the proposed FCN model is similar to that of the CNN model, the former requires only 10% of the network parameters of the CNN model. In addition, our experiments to measure the energy consumption on the embedded platforms showed that the proposed method uses as much as 6.5 times less energy than the CNN-based method when only HAR energy consumption is compared.

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A Novel Energy-Efficient Approach for Human Activity Recognition

Cited by 31 publications

References 38 publications

W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

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